根据查找表替换数据框中的值

Question

我在替换数据帧中的值时遇到了一些麻烦.我想基于单独的表替换值.以下是我想要做的一个例子.

我有一张桌子,每排都是顾客,每列都是他们购买的动物.让我们调用这个数据帧table.

> table
#       P1     P2     P3
# 1    cat lizard parrot
# 2 lizard parrot    cat
# 3 parrot    cat lizard

我还有一个我将引用的表格lookUp.

> lookUp
#      pet   class
# 1    cat  mammal
# 2 lizard reptile
# 3 parrot    bird

我想要做的就是创建一个名为新表new与功能替换所有值table与class列lookUp.我自己尝试使用一个lapply函数,但是我收到了以下警告.

new <- as.data.frame(lapply(table, function(x) {
  gsub('.*', lookUp[match(x, lookUp$pet) ,2], x)}), stringsAsFactors = FALSE)

Warning messages:
1: In gsub(".*", lookUp[match(x, lookUp$pet), 2], x) :
  argument 'replacement' has length > 1 and only the first element will be used
2: In gsub(".*", lookUp[match(x, lookUp$pet), 2], x) :
  argument 'replacement' has length > 1 and only the first element will be used
3: In gsub(".*", lookUp[match(x, lookUp$pet), 2], x) :
  argument 'replacement' has length > 1 and only the first element will be used

关于如何使这项工作的任何想法？

Answer 1

你在问题中发布了一个方法并不错.这是一种熟悉的方法:

new <- df  # create a copy of df
# using lapply, loop over columns and match values to the look up table. store in "new".
new[] <- lapply(df, function(x) look$class[match(x, look$pet)])

另一种更快的方法是:

new <- df
new[] <- look$class[match(unlist(df), look$pet)]

请注意,我[]在两种情况下都使用空括号()来保持结构new(data.frame).

(我使用的df,而不是table和look,而不是lookup在我的答案)

Answer 2

另一种选择是组合tidyr和dplyr

library(dplyr)
library(tidyr)
table %>%
   gather(key = "pet") %>%
   left_join(lookup, by = "pet") %>%
   spread(key = pet, value = class)

Answer 3

任何时候你有两个单独的data.frames,并试图将信息从一个带到另一个,答案是合并.

R所有人都有自己喜欢的合并方法data.table.

此外,因为要做到这一点,以多列,它会以更快的速度melt和dcast-而不是遍历列,一旦它应用于重整的表,然后再重塑.

library(data.table)

#the row names will be our ID variable for melting
setDT(table, keep.rownames = TRUE) 
setDT(lookUp)

#now melt, merge, recast
# melting (reshape wide to long)
table[ , melt(.SD, id.vars = 'rn')     
       # merging
       ][lookup, new_value := i.class, on = c(value = 'pet') 
         #reform back to original shape
         ][ , dcast(.SD, rn ~ variable, value.var = 'new_value')]
#    rn      P1      P2      P3
# 1:  1  mammal reptile    bird
# 2:  2 reptile    bird  mammal
# 3:  3    bird  mammal reptile

如果您发现dcast/ meltbit有点令人生畏,这里的方法只是循环遍历列; dcast/ melt只是回避了这个问题的循环.

setDT(table) #don't need row names this time
setDT(lookUp)

sapply(names(table), #(or to whichever are the relevant columns)
       function(cc) table[lookUp, (cc) := #merge, replace
                            #need to pass a _named_ vector to 'on', so use setNames
                            i.class, on = setNames("pet", cc)])

Answer 4

创建一个命名向量,并循环遍历每一列并匹配,请参阅:

# make lookup vector with names
lookUp1 <- setNames(as.character(lookUp$class), lookUp$pet)
lookUp1    
#      cat    lizard    parrot 
# "mammal" "reptile"    "bird" 

# match on names get values from lookup vector
res <- data.frame(lapply(df1, function(i) lookUp1[i]))
# reset rownames
rownames(res) <- NULL

# res
#        P1      P2      P3
# 1  mammal reptile    bird
# 2 reptile    bird  mammal
# 3    bird  mammal reptile

数据

df1 <- read.table(text = "
       P1     P2     P3
 1    cat lizard parrot
 2 lizard parrot    cat
 3 parrot    cat lizard", header = TRUE)

lookUp <- read.table(text = "
      pet   class
 1    cat  mammal
 2 lizard reptile
 3 parrot    bird", header = TRUE)